Recent Directions in Nonparametric Bayesian Machine Learning

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March 24, 2008

Machine learning is an interdisciplinary field which seeks to develop both the mathematical foundations and practical applications of systems that learn, reason and act. Machine learning draws from many fields, ranging from Computer Science, to Engineering, Psychology, Neuroscience, and Statistics. Because uncertainty, data, and inference play a fundamental role in the design of systems that learn, statistical methods have recently emerged as one of the key components of the field of machine learning. In particular, Bayesian methods, based on the work of Reverend Thomas Bayes in the 1700s, describe how probabilities can be used to represent the degrees of belief of a rational agent. Bayesian methods work best when they are applied to models that are flexible enough to capture to complexity of real-world data. Recent work on non-parametric Bayesian methods provides this flexibility.  Zoubin will touch upon key developments in the field, including Gaussian processes, Dirichlet processes, and the Indian buffet process (IBP). Focusing on the IBP, Zoubin will describe how this can be used in a number of applications such as collaborative filtering, bioinformatics, cognitive modelling, independent components analysis, and causal discovery. Finally, Zoubin will outline the main challenges in the field: how to develop new models, new fast inference algorithms, and compelling applications.


Speaker Photo of Zoubin Ghahramani

Zoubin Ghahramani

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, UK, and is also Associate Research Professor of Machine Learning at Carnegie Mellon University, USA. He obtained BA and BSE degrees from University of Pennsylvania, and a PhD in 1995 from MIT working with Prof Mike Jordan. He did a postdoc in Computer Science at University of Toronto working with Prof Geoff Hinton. His work has included research on human sensorimotor control, cognitive science, statistics, and machine learning. His current focus is on Bayesian approaches to statistical machine learning, and has recently started a research programme on machine learning applications to information retrieval. He has published over 100 peer reviewed papers, and serves on the editorial boards of several leading journals in the field, including JMLR, JAIR, Annals of Statistics, Machine Learning, and Bayesian Analysis. He is Associate Editor in Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence, currently the IEEE's highest impact journal. He also serves on the Board of the International Machine Learning Society, and was Program Chair of the 2007 International Machine Learning Conference. For more information, visit: